NEW!
11. “Tigers” by Bilal Wahib has a tempo of 112 BPM


Another popular track in the corpus is “Tigers” by Bilal Wahib. Tempograms are plotted in order to show the estimated BPM of the track along its duration.

The tempo feature of the Spotify API estimates a BPM of 111.943 (rounded 112 BPM).

The first Tempogram doens’t explicitly reflect the estimation of the Spotify API, tempi of around 210-220 and 430-450 are shown in the plot. Plot might represent record half-time and quarter time BPM of 224 and 448 based on the estimation of 112 BPM.

The second Tempogram (cyclic), is adjusted to represent the more ‘common’ tempi humans tap. This plot does reflect the Spotify API estimation of 112 BPM much clearer.

At the 75 second mark, there is a slight drop and increase in tempo is recorded. From this point a tape stop sound effect is immediately followed by the bridge. The BPM however, remains the same (try to tap along).

NEW!
10. Most prevalent beats:
Tempi around 100 BPM and 120BPM most common in corpus.


The density plot shows that overall the the most frequent tempi within the corpus is around 90-100 BPM and 115-128 BPM. The year 2019 showed a strong preference for tracks around 98 BPM and to a lesser extent 123 BPM.

The year 2020 showed a strong preference for tracks around both 95 BPM and 121 BPM.

The first 7 weeks of 2021 showed a preference for tracks around 99 BPM and 122 BPM.

1. Did Spotify users in the Netherlands change their music listening behavior during the COVID-19 pandemic?

Computational Musicology Portfolio

The COVID-19 pandemic has stirred society up by quite large margin. Many people are (in)directly by the health crisis or the resulting governmental measures. Leading to societal adjustments, e.g. social distancing and isolation, causing society to change communication, work and more aspects of daily life. This dashboard will explore the main question below.

Did Spotify users in the Netherlands change their music listening behavior during the COVID-19 pandemic?

A corpus has been be created to perform various computational musicological analyses using the spotifyr and compmus packages. The listening behavior of Spotify users in the Netherlands before and during the COVID-19 pandemic will be considered, as measured by the Spotify API. In addition, specific events and times will be highlighted in order to find out to what extent changes in listening behavior can be attributed to these events (e.g. lockdown and curfew).

Corpus

In order to analyze general listening behavior, the most important variables for the analyses are:

Playlist

In order to keep track on the average listening behavior of Dutch Spotify users, the weekly ‘Top 50’ and ‘Viral 50’ playlists from the Netherlands will be analyzed over time. The year 2019 will be measured from week 45, the year 2020 in its entirety, and 2021 is measured until week 7.

Thus,
2019 contains 8 playlists consisting of 50 observations (tracks)
2020 contains 53 playlists consisting of 50 observations (tracks)
2021 contains 7 playlists consisting of 50 observations (tracks)

Since Spotify autoupdates their playlists, the historical ‘Top 50’ lists in the form of CSV files will be retrieved from Spotify Charts.

Spotify Audio Features

The changes of (or lack thereof) listening behavior will be measured by the the different Spotify Audio Features:

▶ Spotify Audio Features:
  • danceability
  • energy
  • key
  • loudness
  • mode
  • speechiness
  • acousticness
  • instrumentalness
  • liveness
  • valence
  • tempo
  • duration_ms

Also the following features obtained through the Spotify API will be analyzed:

  • Number of streams
  • Position
  • Track Name
  • Artist
  • Streams

The mean of each variable of the 50 tracks will be calculated, outliers and other interesting tracks in the dataset will be highlighted as well.

Time

The variable time will be used to identify the different weeks as well as the periods before and during the pandemic that may explain the changes in music listening behavior from the top and viral playlists. In addition interesting annual periods will be isolated to see if similar patterns reoccur during the pandemic.For example, the December Holiday season before and during the pandemic will be analyzed to identify whether Spotify users altered their Christmas related listening behavior.

  • Week
  • Year

COVID-19 variables

Alongside the musical analyses the statistics concerning COVID-19 will be taken into account. The data is provided by the The Dutch National Institute for Public Health and the Environment (RIVM). The data has been pre-processed to include weekly and cumulative data. The variables that are included in this dashboard are the following variables:

  • Hospital Admissions
  • Number of deaths
  • Reported cases of COVID-19

2. A trip down memory lane:
What were people in the Netherlands listening to before the pandemic?

Note: tentative



Some text underneath the slider and checkboxes

5. 17 Miljoen Mensen vs. 15 Miljoen Mensen - The prominent cover song during the pandemic shows little similarity with original


The track “17 Miljoen Mensen” (2020) is a cover of “15 Miljoen Mensen” (1996), which was released 24 years earlier! An analysis of the chromafeatures of the two tracks made in order to find similarities between them. Instantly noticed differences are 17 Miljoen mensen’s title adjustment for the population increase of 2 million people, and its shortness with a duration of just 1 minute and 47 seconds.

The first plot shows the Dynamic Time Warping of the two tracks, using Euclidean norm and angular distance. A diagonal pattern would denote similarity between the two tracks. This is not observed, which implies significant differences. For instance, the table below shows that pitch classes differ. According to the Spotify API, “17 Miljoen Mensen” is in the key of G major, wheras “15 Miljoen Mensen” is in the key of C major. This is not explicitly shown, but they are represented in their respective chromagrams.

The tracks do share a In addition the ‘sound and feel’ of the tracks differ: 15 miljoen mensen has a higher danceability, energy, and loudness, whereas “17 miljoen mensen” has a much higher acousticness and liveness (due to the recording being a live performance). A remarkable commonality probably explains the differences: Both tracks were unintended single releases, “15 miljoen mensen” was initially written for a commercial, and “17 Miljoen mensen” as a tribute for a (due to COVID-19) canceled music concert. The different motivations behind the tracks reflects the different ‘sound and feel’ as shown by Spotify API.

17 Miljoen Mensen (2020) 15 Miljoen Mensen (1996)
danceability 0.493 0.547
energy 0.321 0.631
key 7 0
loudness -10.041 -7.063
mode 1 1
speechiness 0.0402 0.0266
acousticness 0.715 0.0943
instrumentalness 0 0
liveness 0.0863 0.0548
valence 0.508 0.481
tempo 86.77 79.02
duration_sec 107.2 236.107
time_signature 4 4

6. All I Want before Christmas… is Christmas
Earlier Christmas in 2020 due to the lockdown.


Christmas songs started to dominate the charts in 2020 from around week 49 until week 53, whereas in 2019 Christmas this phenomenon occurred later. In 2020 it is noticeable that the bottom right corner contain tracks with relatively high BPM, high valence, low energy and low danceability.Duruing these weeks Christmas tracks dominated the charts. In 2019 this phenomenon is very noticeable in week 52, but shows that Christmas slowly started in week 50. Also in 2020, the charts remained similar during the holiday period from week 50 to 53, whereas in 2019 week 52 saw a spike of the Christmas related audio features. This pattern implies more Christmas tracks entered the Top 50.

Interestingly, Mariah Carey’s ‘All I Want for Christmas’ topped the charts for four consecutive weeks in 2020, as opposed to 1 week in 2019.

A Possible explanation is that due to the imposed lockdown and other restrictions, people may have felt a need or desire for the “Christmas Spirit” or “Christmas Vibes” a week earlier than in 2019.

Another interesting discovery is that similar to 2019, the top streams in 2020 decreased in similar fashion. A possible explanation is that people disregarded the lockdown regulations and spent the holiday season with friends and/or family or were preoccupied with other activities to keep in touch with them.

7. Self-Similarity Matrix: “Dance Monkey” Shows repeating pattern and noticeably distinct Millennial Whoop


Dance Monkey

“Dance Monkey” by Tones And I is one of the most popular tracks within the corpus. A structure analysis will show possible patterns of sequences within the track and their relation.

Cepstrogram

The first cepstrogram plot shows the magnitude of each timbre feature per segment of the track. The feature c01 is loudness, c02 is low frequency, c03 is mid frequencies. c04 and up are not defined as straight forward, but they may be implied by keeping track of changes within a track during specific segments. The cepstrogram shows that "Dance Monkey’s timbre features are relatively more defined by c01 to c05.

  • c01 Loudness: The segments reflect the loudness of the track, this is especially noted during the final chorus.
  • c02* Darkness: The segments faintly show a higher magnitude when the bass drum hits. But its omission is noted much clearly during the breakdown starting at 150 seconds.
  • c03* Mid frequency: It’s shown at about 50 seconds and 165 seconds when higher notes are less and more distinct respectively.
  • c04* Attack: This is very prevalent during the intro (vocal stretch fade-in sfx).
  • c05* [Unknown]: It has the highest magnitude at around 150 seconds, noticeable is the loudness of the “Millenial Whoop”.
Self Similarity

The second and third plots are Self Similarity Matrices (SSM); The first being pitch, and the second timbre. These plots show the structure of a track by denoting patterns of similarities that reoccur. Diagonal lines and a checkerboard pattern show similarity and repetition.

The timbre SSM is plotted using Euclidean norm, Euclidean distance and summarized by the mean. The plot shows a faint checkerboard pattern which implies some form of repetition in the track. At the 150 second mark there is a significant timbre difference. This is when the breakdown occurs with the earlier mentioned “Millenial Whoop”.

The pitch SSM is plotted using Euclidean norm, cosine distance and summarized by root mean square. This plot shows a slightly more noticeable checkerboard pattern. At the 150 second mark, the plot shows a significant change.

8. In the mood for which keys?
Chord and Key estimations for “Mood”.


The track Mood by 24kGoldn ft. iann diorr is also one of the identified popular tracks in the corpus. A keygram and chordogram are plotted in order to show the tonal progression of the track by estimating the chords and key for each segment.

The keygram shows that the key E♭ major, G minor, F major, C major, G major, and C♯minor are prevalent keys during the track. The Chordogram show that the chords C minor, E♭ 7, and E♭ major are the most prevalent chords of the track.

Spotify API

According to the Spotify API, this track is written in the 7th key, with mode 0: meaning G minor.

Chordify

The Chordify algorithm identified the chords the following (4/4) loop:

E♭ - Gm - | B♭ - F - |

The identified key appears to be on the natural scale:

G - A - B♭ - C - D - E♭ - F

9. Histogram of Keys within the corpus.


While histogram doesn’t show a clear/unanimous preference, the keys C♯, F♯, G♯ consistently do have a relative high count within the corpus.

In 2019, There is a clear significant higher count of C♯, F, G, B key.

In 2020, The keys C♯, F♯, G♯, B have a significantly higher frequency in the corpus.

Note that 2021 only contains the first 7 weeks, whereas 2019 and 2020 contain 52 and 53 weeks respectively. Therefore, its not very representative to make the most informed comparisons.